To predict \(\mathbf {O}^{tar}_{known} \setminus \mathbf {O}^{in}_{unknown}\) , the known occupancy value in the target that is unknown in the input, we use OPNet [1]} which contains a U-Net [2]} with aspp [3]} to improve the prediction on a multiscale level. As the goal of the occupancy prediction is to provide occupancy information beyond the frontiers for the estimation of information gains, we design a loss function \(\mathcal {L}\)  that contains an occupancy loss \(\mathcal {L}_{occ}\)  that emphasizes the prediction of the occupancy of individual voxels and a structural loss \(\mathcal {L}_{struct}\)  that focuses on the inference of unknown structures. The loss is defined as follows:
\(\mathcal {L} = \omega _{occ} * \mathcal {L}_{occ} + \omega _{struct} * \mathcal {L}_{struct}\) 
